What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Geometry
- Meteorology
Michael Bevis mainly investigates Seismology, GPS meteorology, Subduction, Remote sensing and Precipitable water.
His studies deal with areas such as Kinematics, Moment magnitude scale, Mantle and Geodesy as well as Seismology.
His Geodesy study integrates concerns from other disciplines, such as Episodic tremor and slip and Plate tectonics.
His work on Volcanic arc as part of general Subduction study is frequently linked to Barrier effect and Large size, bridging the gap between disciplines.
His Remote sensing study combines topics in areas such as Geodetic datum and Water vapor.
To a larger extent, Michael Bevis studies Meteorology with the aim of understanding Precipitable water.
His most cited work include:
- GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System (1436 citations)
- GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water (798 citations)
- GPS Meteorology: Direct Estimation of the Absolute Value of Precipitable Water (377 citations)
What are the main themes of his work throughout his whole career to date?
Michael Bevis mainly focuses on Seismology, Geodesy, Geodetic datum, Subduction and Remote sensing.
His Seismology study frequently intersects with other fields, such as New Hebrides.
His research in Geodesy intersects with topics in Bedrock, Gps network, Scotia and Deformation.
The Forearc research Michael Bevis does as part of his general Subduction study is frequently linked to other disciplines of science, such as Trench, therefore creating a link between diverse domains of science.
His Remote sensing research includes themes of Precipitable water and Water vapor.
Water vapor is a subfield of Meteorology that Michael Bevis tackles.
He most often published in these fields:
- Seismology (34.56%)
- Geodesy (32.72%)
- Geodetic datum (13.82%)
What were the highlights of his more recent work (between 2016-2021)?
- Geodesy (32.72%)
- Earth (3.69%)
- Transverse isotropy (3.23%)
In recent papers he was focusing on the following fields of study:
His main research concerns Geodesy, Earth, Transverse isotropy, Seismology and Classical mechanics.
His study in the field of Geodetic datum also crosses realms of State.
His study in Geodetic datum is interdisciplinary in nature, drawing from both Glacial period, Vertical displacement, Data stream mining, Geodynamics and Seismic gap.
Many of his research projects under Seismology are closely connected to Interim report with Interim report, tying the diverse disciplines of science together.
His Subduction research integrates issues from Coseismic slip, Stress relaxation, Fault mechanics and Surface velocity.
His Classical mechanics research incorporates themes from Green S, Computation and Special case.
Between 2016 and 2021, his most popular works were:
- Accelerating changes in ice mass within Greenland, and the ice sheet's sensitivity to atmospheric forcing (73 citations)
- Observed rapid bedrock uplift in Amundsen Sea Embayment promotes ice-sheet stability (69 citations)
- Illuminating subduction zone rheological properties in the wake of a giant earthquake. (15 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Geometry
- Plate tectonics
Ice sheet, Seismology, Glacier mass balance, Classical mechanics and Earth are his primary areas of study.
In his research, Mantle, Antarctic ice sheet, Post-glacial rebound and Glacier is intimately related to Bedrock, which falls under the overarching field of Ice sheet.
His research on Seismology focuses in particular on Subduction.
Michael Bevis has researched Subduction in several fields, including Slab, Large earthquakes, Surface displacement and Moment magnitude scale.
His work carried out in the field of Glacier mass balance brings together such families of science as Climatology and Greenland ice sheet.
His Classical mechanics research is multidisciplinary, relying on both Point and Computation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
